For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning
Zhuo ZHANG Yan LEI Jianjun XU Xiaoguang MAO Xi CHANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/09/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Software Engineering
debugging, fault localization, term frequency, inverse document frequency, deep learning,
Full Text: PDF>>
Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.